Experiments DB Dev
0e9263b958
Add tdcs_model_summary as the single place that renders a fitted model verbatim (disp(model) with the Command-Window <strong> markup stripped), and route every report through it so all scenarios emit the complete model-fitting output: - tdcs_report: adds the learning-rate GLMM's full summary (count/rate already had theirs); localCleanDisp now delegates to the shared helper. - tdcs_lme_report / tdcs_paper_lme: embed the full fitlme summary before the curated effect table. - tdcs_phase_lme: appends each phase's full fitlme summary. Tests: tReport asserts 3 native summaries in a GLMM report; tLme asserts the lme_*/paper_*/phase_* reports embed 1/1/3 summaries with markup stripped. Suite 39/39. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
68 lines
2.6 KiB
Plaintext
68 lines
2.6 KiB
Plaintext
==============================================================================
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PAPER LME REPLICATION -- mergeNaive
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==============================================================================
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model: behavior ~ stim + day + stim:day + (1|rat) [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
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N = 11 rats, 170 sessions (day raw; day 0 = paper "Day 1")
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day coverage: stim(B2) 0..22, control(A2) 0..26
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** WARNING: unequal day coverage -- the full-range stim:day interaction
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extrapolates the control group's line and is CONFOUNDED here (the paper's
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groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
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==============================================================================
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FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
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==============================================================================
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Linear mixed-effects model fit by ML
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Model information:
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Number of observations 170
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Fixed effects coefficients 4
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Random effects coefficients 11
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Covariance parameters 2
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Formula:
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behavior ~ 1 + day*stim + (1 | rat)
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Model fit statistics:
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AIC BIC LogLikelihood Deviance
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1526.4 1545.2 -757.2 1514.4
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Fixed effects coefficients (95% CIs):
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Name Estimate SE tStat DF pValue
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{'(Intercept)'} 30.553 4.7932 6.3742 166 1.7469e-09
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{'day' } 3.5345 0.33399 10.583 166 2.5084e-20
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{'stim' } 10.155 8.0021 1.2691 166 0.2062
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{'day:stim' } 1.5835 0.58611 2.7018 166 0.0076138
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Lower Upper
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21.089 40.016
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2.8751 4.1939
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-5.6439 25.954
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0.42633 2.7407
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Random effects covariance parameters (95% CIs):
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Group: rat (11 Levels)
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Name1 Name2 Type Estimate
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{'(Intercept)'} {'(Intercept)'} {'std'} 9.2449
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Lower Upper
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5.388 15.863
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Group: Error
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Name Estimate Lower Upper
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{'Res Std'} 19.866 17.802 22.168
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effect t (df) F (df1) p
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------------------------------------------------------------------
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stim x day (interaction) t(166)= 2.70 F(1)= 7.299 p=0.007614
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day (learning) t(166)= 10.58 F(1)= 111.990 p=2.508e-20
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stim (main, Day 1) t(166)= 1.27 F(1)= 1.611 p=0.2062
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interaction 95% CI: [+0.43, +2.74]
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Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
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F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.
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